Predicting evolutionary change poses numerous challenges. Here we take advantage of the model bacterium Pseudomonas fluorescens in which the genotype-to-phenotype map determining evolution of the adaptive ‘wrinkly spreader’ (WS) type is known. We present mathematical descriptions of three necessary regulatory pathways and use these to predict both the rate at which each mutational route is used and the expected mutational targets. To test predictions, mutation rates and targets were determined for each pathway. Unanticipated mutational hotspots caused experimental observations to depart from predictions but additional data led to refined models. A mismatch was observed between the spectra of WS-causing mutations obtained with and without selection due to low fitness of previously undetected WS-causing mutations. Our findings contribute toward the development of mechanistic models for forecasting evolution, highlight current limitations, and draw attention to challenges in predicting locus-specific mutational biases and fitness effects.

Predicting evolution might sound like an impossible task. The immense complexity of biological systems and their interactions with the environment has meant that many biologists have abandoned the idea as a lost cause. But despite this, evolution often repeats itself. This repeatability offers hope for being able to spot in advance how evolution will happen. To make general predictions, it is necessary to understand the mechanisms underlying evolutionary pathways, and studying microbes in the laboratory allows for real-time experiments in evolution.

One of the best studied microbes for experimental evolution is Pseudomonas fluorescens, which repeatedly evolves flattened wrinkled colonies instead of round smooth ones when there is limited oxygen. The underlying molecular pathways that lead to this change have been studied in detail.

Lind et al. developed mathematical models to predict how often the three most common pathways would be used and which genes were most likely to be mutated. After controlling for the effects of natural selection and refining the models to take into account mutation hotspots, Lind et al. were able to accurately predict the genes that would be targeted by mutations.

The findings suggest that biologists need not lose hope when it comes to the goal of predicting evolution. A deep understanding of the molecular mechanisms of evolutionary changes are essential to predicting the mutations that lead to adaptive change. The results are an important first step towards forecasting organisms’ responses to changing conditions in the future. In the short term, this is important for medical issues, including antibiotic resistance, cancer and immune receptors. In the long term, predicting the course of evolution could be essential for survival of life on the planet.